Ekundayo E.O. Environmental monitoring

Подождите немного. Документ загружается.

Air Pollution Analysis with a Possibilistic and Fuzzy Clustering Algorithm Applied in a Real Database of Salamanca (México) 11

station we observe that either SO

or PM

pollutant concentrations are highest. At the DIF

monitoring station we observe the highest PM

concentrations in the AEMN network.

The main proposal in this work is to apply the PFCM clustering algorithm to the AEMN in

Salamanca as well to integrate the pollutant measures from the three monitoring stations.

The PFCM initial parameters (a , b, m and η) are very important in order to reduce the outlier

effects in the pattern prototypes. Pal et al, in Pal et al. (2005) recommend of b parameter value

larger than the a parameter value in order to reduce the mentioned effects. On the other hand,

a small value for η and a value greater than 1 for m are recommended. nevertheless, choosing

a too high of a value of m reduces the effect of membership of data to the clusters, and the

algorithm behaves as a simple PCM.

Taking into account the previous recommendations, the initial parameters for the PFCM

clustering algorithm were set as follows: a

= 1, b = 5, m = 2andη = 2. The found prototypes

( a and b)areshowninFig.4.

In Fig. 4(a) the daily averages of SO

concentrations are presented for each monitoring station

together with the corresponding prototypes. It is observed also that Cruz Roja monitoring

station receives the highest emissions of SO

concentrations: this is due to its location near

to the reﬁnery. The prototypes in this case were very low in comparison with the observed

concentrations, because only one station observed high SO

concentrations (Cruz Roja).

According with the analyzed patterns the emitted pollutant is only measured by the Cruz Roja

monitoring station (see Fig. 4).

Fig. 4(b) shows the daily averages of PM

concentrations and result prototypes. In this case,

the observed averages are very similar at the three monitoring stations. The PM

pollutant

dispersion is more uniform then the SO

pollutant dispersion in the city.

Table 2 shows the correlation results among SO

and PM

pollutants and the meteorological

variables. The database used in the correlation analysis correspond to year 2004 of Nativitas.

This period was taking because contains more meteorological registrations. The obtained

results of the SO

correlation coefﬁcient show a high positive correlation between SO

pollutant and Wind Speed, also a high and negative correlation between SO

pollutant and

Wind Direction is observed. The other meteorological variables have not impact. For the

pollutant, the meteorological variable with more impact is the Relative Humidity. We

observe, when the Relative Humidity increases the pollutant concentration decreases. The

particles are caught and fall to the ground during rain.

1 0.0731

0.0731 1

WS 0.4756 -0.1385

WD -0.6151 0.1478

T -0.0329 -0.0007

RH -0.0322 -0.4416

BP 0.1462 0.1806

SR -0.021 -0.1207

Table 2. Correlation Coefﬁcient between pollutant concentration and meteorological

variables.

Air Pollution Analysis with a Possibilistic

and Fuzzy Clustering Algorithm Applied in a Real Database of Salamanca (México)

12 Environmental Monitoring

0 5 10 15 20 25 30

100

120

Number of Days

Concentration (ppb)

Comparison among monitoring points and prototype

Prototype

(a) SO

0 5 10 15 20 25 30

100

120

Number of Days

Concentration( μ gr/m

)

Comparison among monitoring points and prototype

Prototype

(b) PM

Fig. 4. Comparison between air pollutant averages and estimated prototypes.

Environmental Monitoring

Air Pollution Analysis with a Possibilistic and Fuzzy Clustering Algorithm Applied in a Real Database of Salamanca (México) 13

5. Conclusions

Nowadays, there is a program to improve the air quality in the city of Salamanca, Mexico.

Besides, this program has established thresholds for several levels of contingencies depending

on the SO

and PM

pollutant concentrations. However, a particular level of contingency for

the city is declared taking into account the highest pollutant concentration provided by one

of the three monitoring stations. For example, if a pollutant concentration exceeds a given

threshold in a single monitoring station, the alarm of contingency applies to the whole city.

This value is normally provided by the Cruz Roja station, due to its proximity to the reﬁnery

and power generation industries.

Looking for local and general contingency levels in the city, we have proposed to estimate a

set of prototypes such that they can represent a calculated measure of pollutant concentrations

according to the values measured in the three ﬁxed stations. In such a way, a local alarm of

contingency can be activated in the area of impact of the pollution depending on each station,

and a general alarm of contingency according to the values provided by the prototypes.

Nevertheless, the last case requires adjusting the thresholds, as the actual values would be

only used for local contingency because they depend on the measured values of pollutant

concentrations, and the general contingency requires thresholds as a function of calculated

values.

6. References

Andina, D. & Pham, D. T. (2007). Computational Intelligence,Springer.

Barron-Adame, J. M., Herrera-Delgado, J. A., Cortina-Januchs, M. G., Andina, D. &

Vega-Corona, A. (2007). Air pollutant level estimation applying a self-organizing

neural network, Proceedings of the 2nd international work-conference on Nature Inspired

Problem-Solving Methods in Knowledge Engineering. IWINAC-07, pp. 599–607.

Bezdek, J. C. (1981). Pattern Recognition With Fuzzy Objective Function Algorithms,Kluwer

Academic.

Bezdek, J. C., Keller, J., Krishnapuram, R. & Pal, N. R. (1999). Fuzzy Models and Algorithms for

Pattern Recognition and Image Processing,ﬁrstedn,Boston,London.

Celik, M. B. & Kadi, I. (2007). The relation between meteorological factors and pollutants

concentration in karabuk city, G.U. Journal of science 20(4): 87–95.

Cortina-Januchs, M. G., Barron-Adame, J. M., Vega-Corona, A. & Andina, D. (2009). Prevision

of industrial so2 pollutant concentration applying anns, Proceedings of The 7th IEEE

International Conference on Industrial Informatics (INDIN 09), pp. 510–515.

Dunn, J. (1973). A fuzzy relative of the isodata process and its use in detecting compact

well-separated clusters, Journal of Cybernetics 3(3): 32–57.

EPA (2008). Air quality and health, chapter Environmental Protection Agency, National

Ambient Air Quality Standards (NAAQS).

Fenger, J. (2009). Air pollution in the last 50 years - from local to global, Journal of Atmospheric

Environment 43(1): 13–22.

Hoppener, F., Klawonn, F., Kruse, R. & Runkler, T. (2000). Fu zzy Cluster Analysis, Methods for

classiﬁcation, data analysis and image recognition, Chistester, United Kingdom.

INE (2004). Programa para mejorar la calidad del aire en Salamanca, 2 edn, Instituto de Ecología del

Estado de Guanajuato, Calle Aldana N.12, Col. Pueblito de Rocha, 36040 Guanajuato,

Gto.

Air Pollution Analysis with a Possibilistic

and Fuzzy Clustering Algorithm Applied in a Real Database of Salamanca (México)

14 Environmental Monitoring

INEGI (2005). National Population and Housing Census 2, National Institute of Geography and

Statistics. www.inegi.org.mx.

Krishnapuram, R. & Keller, J. (1993). A possibilistic approach to clustering, International

Conference on Fuzzy Systems 1(2): 98–110.

Krishnapuram, R. & Keller, J. (1996). The possibilistic c-means algorithm: Insights and

recommendations, International Conference on Fuzzy Systems 4, no 3: 385–393.

Ojeda-Magaña, B., Quintanílla-Dominguez, J., Ruelas, R. & Andina, D. (2009b). Images

sub-segmentation with the pfcm clustering algorithm, Proceedings of The 7th IEEE

International Conference on Industrial Informatics (INDIN 09), pp. 499–503.

Ojeda-Magaña, B., Ruelas, R., Buendía-Buendía, F. & Andina, D. (2009a). A greater knowledge

extraction coded as fuzzy rules and based on the fuzzy and typicality degrees

of the GKPFCM clustering algorithm, In Intelligent Automation and Soft Computing

15(4): 555–571.

Pal, N. R., Pal, S. K. & Bezdek, J. C. (1997). A mixed c-means clustering model, IEEE

International Conference on Fuzzy Systems, Spain, pp. 11–21.

Pal, N. R., Pal, S. K., Keller, J. M. & Bezdek, J. C. (2004). A new hybrid c-means

clustering model., Proceedings of the IEEE International Conference on Fuzzy Systems,

FUZZ-IEEE04, I. Press, Ed.

Pal, N. R., Pal, S. K., Keller, J. M. & Bezdek, J. C. (2005). A possibilitic fuzzy c-means clustering

algorithm, IEEE Transactions on Fuzzy Systems 13(4): 517–530.

Pérez, P., Trier, A. & Reyes, J. (2000). Prediction of pm 2.5 concentrations several hours

in advance using neural networks in santiago, chile, Atmospheric Environment

34(8): 1189–1196.

Ruspini, E. (1970). Numerical method for fuzzz clustering, Information Sciences 2(3): 319–350.

Timm, H., Borgelt, C., Dör ing, C. & Kruse, R. (2004). An extension to possibilistic fuzzy cluster

analysis, Fuzzy Sets and systems 147, no 1: 3–16.

Timm, H. & Kruse., R. (2002). A modiﬁcation to improve possibilistic fuzzy cluster analysis.,

Conference Fuzzy Systems, FUZZ-IEEE, Honolulu, HI, USA.

WHO (2008). Air quality and health, chapter World Health Organization.

Zamarripa, A. & Sainez, A. (2007). Medio Ambiente: Caso Salamanca, Instituto de Investigación

Legistativa, H. Congreso del Estado de Guanajuato, LX legislatura.

Zuk, M., Cervantes, M. G. T. & Bracho, L. R. (2007). Tercer almanaque de datos y tendencias de

la calidad del aire en nueve ciudades mexicanas, Technical report, Secretaría de Medio

Ambiente, Recursos Naturales Instituto Nacional de Ecología, México, D.F.

Environmental Monitoring

Real-Time In Situ Measurements

of Industrial Hazardous Gas

Concentrations and Their Emission Gross

F.Z. Dong et al.

Anhui Institute of Optics and Fine Mechanics,

Chinese Academy of Sciences, Science Island, Hefei,

P. R. China

1. Introduction

Over the past few decades environmental protection has been of greatly worldwide

concerns due to the fact of global warming and air quality deterioration particularly in the

fast developing countries like China and India (Platt, 1980; Edner, 1991; Sigrist, 1995;

Culshaw, 1998; Fried, 1998; Linnerud, 1998; Weibring, 1998; Nelson, 2002; Liu, 2002;

Christian, 2003 & 2004; Taslakov, 2006; de Gouw, 2007; Karl, 2007 & 2009;

http://www.cnemc.cn). These have resulted in large demands and tremendous efforts for

new technology developments to monitor and control industrial gas pollution (Lindinger,

1998; Dong, 2005; Kan, 2006 & 2007; Wang Y.J., 2009; Wang F., 2010; Xia, 2010; Zhang, 2011).

, CO, NH

, H

S, HF, HCI, and volatile organic compounds (VOCs) are very important

gases generated in many industrial processes; therefore to implement on-line monitoring of

these industrial emitted gases is a key factor for industrial process control. Furthermore if

one can simultaneously measure the gas flow path-averaged velocity and gas concentrations

in a smokestack, all the industrial emissions from the targeted smokestack would be real-

time obtained. This could be much beneficial to the administrative implementation of global

environmental protection policy on reduction of gas pollution and environmental

management.

Tunable diode laser absorption spectroscopy (TDLAS) is a kind of technology with advantages

of high sensitivity, high selectivity and fast responsibility. It has been widely used in the

applications of green-house measurements (Feher, 1995; Nadezhdinskii, 1999; Kan, 2006),

hazardous gas leakage detection (May, 1989; Uehara, 1992; Iseki, 2000 & 2004), industry

process control (Linnerud, 1998; Deguchi,2002) and combustion gas measurements (Zhou,

2005; Rieker, 2009). Proton transfer reaction—mass spectrometry (PTR-MS) is a relatively new

technology firstly developed at the University of Innsbruck, Austria, in the 1990s (Hansel,

1995). PTR-MS has been found being an extremely powerful and promising technology for on-

line detection of VOCs at trace level (Smith, 2005; Jordan, 2009). Optical flow sensor (OFS-2000)

based on the concept of optical scintillation to measure airflow velocity (Wang T.I., 1981;

* W.Q. Liu, Y.N. Chu, J.Q. Li, Z.R. Zhang, Y. Wang, T. Pang, B. Wu, G.J. Tu, H. Xia, Y. Yang,

C.Y. Shen, Y.J. Wang, Z.B. Ni and J.G. Liu

Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Science Island, Hefei, P. R. China.

Environmental Monitoring

http://www.opticalscientific.com), which is first developed by Optical Scientific INC., has

been widely used in the market. OFS-2000 utilizes the high frequency signal of optical

scintillation cross-correlation (OSCC) which is from the fluctuations of temperature or

refractive index. However, OFS-2000 is not applicable when the temperature fluctuation

within the measurement area is small or even ignorable. Recently we have developed a new

kind of optical flow sensor which is based on the low frequency signal of OSCC resulting from

the particle concentration fluctuations. Therefore the newly developed optical flow sensor

could also measure the particle concentration in the stack.

The content of this chapter will first briefly describe the operational principles based on

TDLAS, PTR-MS and OSCC technologies for industrial pollution on-line monitoring. Then

the instruments developed by our group to measure the emission gross will be introduced.

In the third section some experimental results from the field test will be presented. Finally

the discussions and conclusions will be given.

2. Basic operational principles of the instruments

2.1 TDLAS technique

For detecting low concentration gases at atmospheric pressure with TDLAS technique, two-

tone modulations and harmonic detection method are commonly adopted. The diode laser

is modulated with the homemade current and temperature controllers to the wavelength of

1.567μm which precisely locates at the selected absorption line central of target gas CO. The

laser wavelength is scanned through the selected absorption line by a saw-tooth signal at

low frequency of 147Hz and simultaneously modulated by a sinusoidal signal at frequency

of 20 KHz. The modulated laser beam is divided into two parts with a 1×2 fiber splitter. One

arm (20%) is used to go through a 10cm calibration cell as a reference signal, while the other

arm (80%) is used to measure the flue gas concentrations. Two transmitted laser beams are

collimated and then collected by two coincident InGaAs photodiodes after passing through

absorption gases, respectively. These two current signals are then transmitted into the

digital control module (DCM) to gain the harmonic signals. At last, these signals are sent to

computer for processing and harmonic signal detection technique is used for calculation of

the target gas concentration. The schematic diagram of the online TDLAS experimental

setup is shown in Figure 1.

flue gas

DFB-Laser

TEC

CUR

Digital

control module

16bit 1f、2f harmonic signals

collimating system

focusing system

Fiber

splitting

detector

Calibration cell

Serial port

acquisition

flue gas

DFB-Laser

TEC

CUR

Digital

control module

16bit 1f、2f harmonic signals

collimating system

focusing system

Fiber

splitting

detector

Calibration cell

Serial port

acquisition

TEC: thermo-electronic cooler; CUR: current controller.

Fig. 1. On-line experimental apparatus for TDLAS system.

Real-Time In Situ Measurements

of Industrial Hazardous Gas Concentrations and Their Emission Gross

When the light passes through flue gases, lots of factors can reduce the light intensity, like

dust scattering and absorption in transmission medium. Considering about the intensity

reduction by gas absorption, Beer-Lambert law is used. The responses can be described as:

exp( )



(1)

Where I represents the light intensity after passing the absorption gas, and I

represents the

light intensity before passing the absorption gas, k is a reducing coefficient and L denotes the

path length. When the gas absorption is very small, i.e., kL≤0.05 (Reid, 1981; Cassidy, 1982),

equation (1) can be simplified as:

11()

kL CL



 

(2)

Where σ(ν) is the absorption coefficient. C and L stand for gas concentration and total

optical length. The intensity of second harmonic (2f) signal can be expressed as below (Reid,

1981; Kan, 2006):

∝ I

CL (3)

Where I

is proportional to the incident laser intensity I

and absorption coefficient σ

at the

central wavelength of the

absorption line.

Nonlinear least square multiplication method is

used to fit the 2f signal with reference signal for gaining the calibration coefficient a (Kan,

2007):

Mea

= a I

Ref

(4)

Where C

Mea

and C

Ref

are the concentrations of the target gas to be measured and reference

gas in the calibration cell, respectively; I

, I

are the initial intensities of the two laser

beams; L

and L

are the length of measurement optical path and the calibration cell,

respectively. From equation (4), we could obtain:

Mea

= a I

Ref

/ I

(5)

While a saw-tooth current is added on the DFB diode laser, the light wavelength will scan in

a certain region, then the gas can be detected if there is a gas absorption line in that region.

For detection of high concentration gas, direct absorption method is often used. This method

is very simple but the sensitivity is suffered from massive random noises, which is mainly

the 1/f noise from the diode laser and the photon detector. However, for low concentration

gas detection, in order to eliminate serious noises in the system and enhance the sensitivity,

another high frequency sine modulation current is added on the ramp signal. The gas

absorption signal can be then achieved with high SNR by monitoring the second harmonic

signal of absorption in a very narrow frequency band using a lock-in amplifier (LIA). If one

does not pay enough attention, there will be so many factors like dust scattering and

imperfect performance of laser source itself affecting the measurement accuracy. In addition,

for a practical TDLAS system there are always various noises inevitably existed resulting

from predictable or unpredictable sources. For instance, quickly changing random noise

affects the sensitivity, and slow signal distortion limits long-term stability of the system

Environmental Monitoring

because of its large amplitude. It has been reported that a lot of reasons like wavelength

drifts and etalon fringe structure change because of thermal effect can result to slow 2f

signal distortion (Werle, 1996). Few technologies had been reported to eliminate those

distortions like rapid background subtraction (Cassidy & Reid, 1982) and digital signal

processing (Reid, 1980), but there are some limits of those ideas when the condition is

changed. In fact it is inconvenient to get the background structure in real time for a in situ

gas analytical system, particularly when the interference or distortion has similar frequency

with the absorption signal in which the digital method could not work well.

Over the past decades many advanced digital signal processing methods for TDLAS system

development have been reported. Peter Werle et al (Werle, 1996 & 2004) have demonstrated a

method to avoid the effects of noise disturbances and laser wavelength drifts during

integration and background changes. To decrease high frequency noise and enhance the

stability of a practical TDLAS system, except of optimizing hardware, advanced signal

processing algorithm is also needed and have been explored by our group

(Xia，2010；Zhang，2010). One of the novel features in our research is the use of digital

signal processing for harmonic signals for which the laser output wavelength can be locked at

the absorption line center and fit with reference harmonic signal by utilizing nonlinear least

squares routine. The signal-correlation must be computed rapidly. The Fast Fourier Transform

(FFT), low-pass filter and Inverse Fast Fourier Transform (IFFT) algorithm are adopted. The

correlation version for an N-point spectrum signal is:

(,)

Ref Ref

Mea Mea

jij

CSS S S S S





 



(6)

Where S

Ref

and S

Mea

are the reference and measurement signals acquired during calibration

and subsequent measured harmonic signal, respectively with the lag represented by i. Using

the discrete FFT the correlation signal C(S, S)

can be written as:

(,) ( ) ( )

Ref

ij j

CSS F S F S

(7)

where F

(S) stands for the FFT of S. The low pass filter is used to remove high frequency

noise simultaneously in the process. The IFFT result between measured signal and reference

signal in the above process is used to get the correlation data. Then using the peak-find

routine the drift MAX-value position is obtained. At last, the corrected signal position is

translated getting the proper data to decrease effects caused by the temperature, current and

other external uncertain factors.

2.2 PTR-MS

Proton transfer reaction mass spectrometry (PTR-MS) was first developed at the Institute of

Ion Physics of Innsbruck University in the 1990’s. Nowadays PTR-MS has been a well-

developed and commercially available technique for the on-line monitoring of trace volatile

organic compounds (VOCs) down to parts per trillion by volume (ppt) level. PTR-MS has

some advantages such as rapid response, soft chemical ionization (CI), absolute

quantification and high sensitivity. In general, a standard PTR-MS instrument consists of

external ion source, drift tube and mass analysis detection system. Fig. 2 illustrates the basic

composition of the PTR-MS instrument constructed in our laboratory using a quadrupole

mass spectrometer as the detection system.

Real-Time In Situ Measurements

of Industrial Hazardous Gas Concentrations and Their Emission Gross

Fig. 2. Schematic diagram of the PTR-MS instrument that contains a hollow cathode (HC), a

source drift (SD) region, an intermediate chamber(IC) and a secondary electron multiplier

(SEM).

Perhaps the most remarkable feature of PTR-MS is the special chemical ionization (CI) mode

through well-controlled proton transfer reaction, in which the neutral molecule M may be

converted to a nearly unique protonated molecular ion MH

. This ionization mode is

completely different from the traditional MS where electron impact (EI) with energy of

70 eV is often used to ionize chemicals like VOCs. Although the EI source has been widely

used with the commercial MS instruments most coupled with a variety of chromatography

techniques, these MS platforms have a major deficiency: in the course of ionization the

molecule will be dissociated to many fragment ions. This extensive fragmentation may

result in complex mass spectra pertain especially when a mixture is measured. If a

chromatographic separation method is not used prior to MS, then the resulting mass spectra

from EI may be so complicated that identification and quantification of the compounds can

be very difficult. In PTR-MS instrument, the hollow cathode discharge is served as a typical

ion source [Blake, 2009], although plane electrode dc discharge [Inomata, 2006] and

radioactive ionization sources [Hanson, 2003] recently have been reported. All of the ion

sources are used to generate clean and intense primary reagent ions like H

. Water vapor

is a regular gas in the hollow cathode discharge where H

O molecule can be ionized

according to the following ways (Hansel, A.,1995).

e+H

O → H

+O+2e (8)

e+ H

O → H

+ OH+2e (9)

e+H

O → O

+ H

+ 2e (10)

e+H

O → H

+2e (11)

Environmental Monitoring

The above ions are injected into a short source drift region and further react with H

ultimately leading to the formation of H

via ion-molecule reactions:

O → H

(12a)

→ H

+H (12b)

O → H

+H (13)

O → H

+O (14)

O → H

+O (15a)

→ H

+OH (15b)

O → H

+OH (16)

Unfortunately, the water vapor in the source drift region can inevitably form a few of cluster

ions H

via the three-body combination process

n-1

O+A→ H

+A (n≥1) (17)

where A is a third body. In addition there are small amounts of NO

and O

ions occurred

due to sample air diffusion into the source region from the downstream drift tube. Thus an

inlet of venturi-type has been employed on some PTR-MS systems to prevent air from

entering the source drift region (Duperat, 1982; Lindinger, 1998). At last the H

ions

produced in the ion source can have the purity up to > 99.5%. Thus, unlike SIFT-MS

technique (Smith, 2005), the mass filter of the primary ionic selection is not needed and the

ions can be directly injected into the drift tube. In some of PTR-MS, the ion intensity of

is available at 10

~10

counts per second on a mass spectrometer installed in the

vacuum chamber at the end of the drift tube. Eventually the limitation of detection of PTR-

MS can reach low ppt level.

Instead of H

, other primary reagent ions, such as NH

, NO

and O

, have been

investigated in PTR-MS instrument (Wiche, 2005; Blake, 2006; Jordan, 2009). Because the ion

chemistry for these ions is not only proton transfer reaction, the technique sometimes is

called chemical ionization reaction mass spectrometry. However, the potential benefits of

using these alternative reagents usually are minimal, and to our knowledge, H

is still the

dominant reagent ion employed in PTR-MS research (Blake, 2009; Lindinger, 1998; de

Gouw, 2007; Jin, 2007).

The drift tube consists of a number of metal rings that are equally separated from each other

by insulated rings. Between the adjacent metal rings a series of resistors is connected. A high

voltage power supplier produces a voltage gradient and establishes a homogeneous electric

field along the axis of the ion reaction drift tube.

The primary H

ions are extracted into the ion reaction region and can react with analyte

M in the sample air, which through the inlet is added to the upstream of the ion reaction

drift tube. According to the values of proton affinity (PA) (see Table 1), the reagent ion H

does not react with the main components in air like N

, O

and CO

. In contrast, the reagent

ion can undergo proton transfer reaction with M as long as the PA of M exceeds that of H

(Lindinger, 1998).